The study investigates AI-powered adaptive learning's effects on three important academic outcomes-student engagement, learning satisfaction, and academic performance-among management students. It further examines the mediating roles of engagement and satisfaction, grounded in Self-Determination Theory (SDT). Management students who have worked with AI-powered learning systems were the subjects of a cross-sectional quantitative study. A structured questionnaire with validated constructs was distributed to a purposive sample. By analyzing direct and indirect interactions, Structural Equation Modeling (SEM) was used to evaluate the suggested conceptual model. The results show that students' engagement, happiness with learning, and academic achievement are much improved by adaptive learning driven by AI. In line with the motivational assumptions of SDT, it was discovered that student engagement and learning satisfaction partly mediate the link between adaptive learning and academic results. In order to improve the management education experience, the results include practical advice for teachers, curriculum designers, and edtech developers. AI Personalization tools can bring about better student achievement scores and enhance the student satisfaction. This study contributes to the expanding body of empirical research on AI-driven adaptive learning by investigating how it affects important learning objectives in management education. It addresses a literature gap by combining robust statistical approach with SDT to explain a learning behaviour of a student in AI enhanced environment